Transportation planning implications of automated/connected vehicles on Texas highways.
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Summary
This report, produced by the Texas A&M Transportation Institute in cooperation with the Texas Department of Transportation and the Federal Highway Administration, addresses the transportation planning implications of automated and connected vehicles (AV/CVs) on Texas highways. The research was motivated by the potential of these technologies to transform the transportation system through increased mobility, safety, and environmental benefits, while acknowledging significant uncertainties regarding their future impact. The study aimed to determine how AV/CVs could be integrated into transportation planning processes to inform decision-making despite these uncertainties. The research team conducted seven primary tasks to assess these implications. First, they defined existing and future AV/CV technologies through literature reviews and previous research. Second, they examined potential impacts on travel behavior, urban form, and commercial freight transportation. Third, they analyzed the effects of AV/CVs on travel forecasting by conducting experimental model runs using a trip-based model from the Austin, Texas region. Fourth, they conducted a statewide web-based survey to gauge behavioral preferences and intent to use self-driving vehicles. Finally, they held three stakeholder workshops in spring 2016 to gather insights on topics such as shared mobility, automated freight, and public transportation impacts. Key findings from the Austin region modeling indicated that AV/CV adoption could significantly alter vehicle miles of travel (VMT), congestion levels, and trip characteristics. The study identified specific changes in speed distributions, delay, and average trip lengths under various AV/CV scenarios compared to a base forecast. The behavioral survey revealed that intent to use self-driving vehicles varied by demographic factors, including age, gender, income, and technology adoption rates, as well as psychological factors like desire for control and perceived safety. Stakeholder workshops highlighted concerns and opportunities related to "robo-taxi" services, automated package delivery, and the potential for automation to reshape public transit investments. The report also detailed how AV/CVs could address specific freight issues, such as driver availability, congestion, and emissions, through technologies like truck platooning and automated warehousing. The significance of this research lies in its provision of a framework for integrating AV/CV considerations into the transportation planning process. The authors recommend steps such as researching and monitoring AV/CV data, forecasting usage and impacts, and performing scenario planning to address the uncertain future of automation. By cross-mapping AV/CV technology with MAP-21 goals, the report suggests that planners can better prepare for potential transformations in the transportation system. The findings underscore the need for adaptive planning strategies that account for the complex interplay between technology adoption, behavioral changes, and infrastructure needs, ensuring that transportation systems remain resilient and efficient in an increasingly automated environment.
Key finding
AV/CV technologies have the potential to significantly alter travel behavior, urban form, and freight logistics, necessitating a shift in transportation planning processes to incorporate scenario planning for uncertainty.
Methodology
mixed_methods
Provenance
The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via bulk_ingest_rosap on 2026-05-23 (6 acquisition events logged).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | rosap | — | — | 2 | 2026-05-23 |
| archive | success | — | — | — | 1 | 2026-05-23 |
| extract | success | cached | — | — | 2 | 2026-06-10 |
| clean | success | — | — | — | 1 | 2026-06-01 |
| chunk | success | — | — | — | 1 | 2026-06-01 |
| embed | success | — | — | — | 1 | 2026-06-02 |
| enrich | success | — | — | — | 1 | 2026-05-23 |
| promote | success | — | — | — | 1 | 2026-05-23 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 3 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 19 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
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- Empirical Findings: observational prevalence
- Theoretical Contribution: conceptual framework